Ray is the only framework that can distribute all AI workloads from multimodal data processing to LLM inference – batch or online. But power doesn’t always mean agility or efficiency. Running Ray in production requires expertise, and managing it yourself can slow progress and inflate engineering costs.
This ebook provides an introduction to Ray and explains how Anyscale removes the barriers that prevent fast iteration and streamlined path to production from day one.
Differences between Ray Core + Ray Libraries (like ray.train, ray.data, ray.serve) for distributed Python workloads
The real-world hurdles developers experience when moving from dev to prod (log aggregation, cluster upgrades, multi-tenant clusters)
What purpose-built functionality is part of the Anyscale platform and how it helps teams build faster and operate more efficiently
Resources to help you get started
This isn’t marketing spin. It’s a practical breakdown of key concepts and what to expect on your Ray adoption journey. By the end, you’ll know if managing Ray yourself is the right call – or if the path to production makes more sense for your organization with Anyscale’s managed platform.